MODELING STABILIZING SELECTION: EXPANDING THE ORNSTEIN—UHLENBECK MODEL OF ADAPTIVE EVOLUTION

Comparative methods used to study patterns of evolutionary change in a continuous trait on a phylogeny range from Brownian motion processes to models where the trait is assumed to evolve according to an Ornstein—Uhlenbeck (OU) process. Although these models have proved useful in a variety of context...

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Bibliographic Details
Published in:Evolution Vol. 66; no. 8; pp. 2369 - 2383
Main Authors: Beaulieu, Jeremy M., Jhwueng, Dwueng-Chwuan, Boettiger, Carl, O'Meara, Brian C.
Format: Journal Article
Language:English
Published: Malden, USA Wiley Subscription Services, Inc 01.08.2012
Blackwell Publishing Inc
Oxford University Press
Subjects:
Adaptation, Physiological
Angiosperms
Biological Evolution
Brownian motion
comparative method
continuous characters Hansen model
Datasets
Evolution
Evolutionary biology
Flowering plants
Genome Size
Genome, Plant
Genomics
Magnoliopsida - genetics
Modeling
Models, Genetic
Ornstein–Uhlenbeck
Parametric models
Phenotype
Phylogenetics
Phylogeny
Selection, Genetic
Simulation
Simulations
Stochastic models
Taxa
ISSN:0014-3820, 1558-5646, 1558-5646
Online Access:Get full text
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Summary:Comparative methods used to study patterns of evolutionary change in a continuous trait on a phylogeny range from Brownian motion processes to models where the trait is assumed to evolve according to an Ornstein—Uhlenbeck (OU) process. Although these models have proved useful in a variety of contexts, they still do not cover all the scenarios biologists want to examine. For models based on the OU process, model complexity is restricted in current implementations by assuming that the rate of stochastic motion and the strength of selection do not vary among selective regimes. Here, we expand the OU model of adaptive evolution to include models that variously relax the assumption of a constant rate and strength of selection. In its most general form, the methods described here can assign each selective regime a separate trait optimum, a rate of stochastic motion parameter, and a parameter for the strength of selection. We use simulations to show that our models can detect meaningful differences in the evolutionary process, especially with larger sample sizes. We also illustrate our method using an empirical example of genome size evolution within a large flowering plant clade.
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ISSN:0014-3820
1558-5646
1558-5646
DOI:10.1111/j.1558-5646.2012.01619.x